LEAD: An EEG Foundation Model for Alzheimer's Disease Detection
cs.LG, cs.AI, cs.CE, eess.SP
Submitted: 2025-02-02
Updated: 2026-09-25
Comments: Accepted by Transactions on Machine Learning Research (TMLR 2026)
Code: https://github.com/DL4mHealth/LEAD
License: http://creativecommons.org/licenses/by/4.0/
The gist: Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD).
Terminology
Abstract
Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). However, existing methods, whether based on handcrafted feature engineering or standard deep learning, face three major challenges: 1) the lack of large-scale EEG-based AD datasets for robust representation learning and evaluation; 2) limited cross-subject generalizability; and 3) difficulty in adapting to highly heterogeneous data. To address these challenges, we curate the world's largest EEG-AD corpus to date, comprising 2,238 subjects. Leveraging this unique resource, we propose LEAD, the first foundation model for EEG-based AD detection. Specifically, we design a gated temporal-spatial Transformer that can adapt to EEG recordings with diverse lengths, channel configurations, and sampling rates. In addition, we introduce a subject-regularized training strategy to enhance end-to-end subject-level detection. We further employ medical contrastive learning to pre-train on 13 datasets, including 4 AD datasets and 9 non-AD neurological disorder datasets, and fine-tune/test the model on the other 5 AD datasets. LEAD achieves the best average ranking across all 20 evaluations on 5 downstream datasets, substantially outperforming existing approaches, including state-of-the-art (SOTA) EEG foundation models. These results strongly demonstrate the effectiveness of our proposed method and significant progress for EEG-based AD detection. Source code: https://github.com/DL4mHealth/LEAD
Sources
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